Matsubara T, Knoblauch J, Briol FX, Oates CJ. Robust Generalised Bayesian Inference for Intractable Likelihoods. Journal of the Royal Statistical Society (Series B), 84(3):997-1022.

[Journal] [arXiv] [Video]

ISBA 2021 Best Student/Postdoc Contributed Paper Award

Best Student Paper Award, ASA Section on Bayesian Statistical Science, 2022

This work has been presented as a conference abstract at the NeurIPS 2021 Workshop “Your Model is Wrong: Robustness and Misspecification in Probabilistic Modeling”. [Web]

Riabiz M, Chen WY, Cockayne J, Swietach P, Niederer SA, Mackey L, Oates CJ. Optimal Thinning of MCMC Output. Journal of the Royal Statistical Society (Series B), 84(4):1059-1081.

[Journal] [arXiv] [Software] [Blog1] [Blog2] [Video]

This work has been presented as a conference abstract at the Third Symposium on Advances in Approximate Bayesian Inference (AABI 2020). [Web] [Video]

South LF, Karvonen T, Nemeth C, Girolami M, Oates CJ. Semi-Exact Control Functionals From Sard's Method. Biometrika, 109(2):351–367.

[Journal] [arXiv] [Software]

Si S, Oates CJ, Duncan AB, Carin L, Briol F-X. Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization. Proceedings of the 14th International Conference in Monte Carlo & Quasi-Monte Carlo Methods in Scientific Computing, Springer 2022.

[Book] [arXiv] [Video]

South LF, Riabiz M, Teymur O, Oates CJ. Post-Processing of MCMC. Annual Reviews of Statistics and its Application, 9:529-555.

[Journal] [arXiv]

Oates CJ, Kendall WS, Fleming L. A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel. Technometrics, 64(3):370-383.

[Journal] [arXiv]

This work has been presented as a conference abstract: Oates CJ, Kendall WS, Fleming L. Generative Modelling of Rough Surfaces: An Application to 3D-Printed Stainless Steel. NeurIPS 2020 Workshop on Machine Learning for Engineering Modeling, Simulation, and Design. [Web]

Barp A, Oates CJ, Porcu E, Girolami M. A Riemann--Stein Kernel Method. Bernoulli, 28(4): 2181-2208.

[Journal] [arXiv]

Dodwell TJ, Fleming LR, Buchanan C, Kyvelou P, Detommaso G, Gosling PD, Scheichl R, Kendall WS, Gardner L, Girolami MA, Oates CJ. A Data-Centric Approach to Generative Modelling for 3D-Printed Steel. Proceedings of the Royal Society A, 477(2255).

[Journal]

Cockayne J, Ipsen ICF, Oates CJ, Reid TW. Probabilistic Iterative Methods for Linear Systems. Journal of Machine Learning Research, 22(232):1-34.

[Journal] [arXiv]

Wang J, Cockayne J, Chkrebtii O, Sullivan TJ, Oates CJ. Bayesian Numerical Methods for Nonlinear Partial Differential Equations. Statistics and Computing, 31(55).

[Journal] [arXiv]

Matsubara T, Oates CJ, Briol F-X. The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks. Journal of Machine Learning Research, 22(157):1−57.

[Journal] [arXiv] [Video]

Karvonen T, Oates CJ, Girolami M. Integration in Reproducing Kernel Hilbert Spaces of Gaussian Kernels. Mathematics of Computation, 90(331):2209-2233.

[Journal] [arXiv]

Teymur O, Gorham J, Riabiz M, Oates CJ. Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. International Conference on Artificial Intelligence and Statistics (AISTATS 2021)

[Journal] [arXiv]

Stephenson V, Oates CJ, Finlayson A, Thomas C, Wilson K. Causal Graphical Models for Systems-Level Engineering Assessment. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(2):04021011.

[Journal] [Preprint]

Prüher J, Karvonen T, Oates CJ, Straka O, Särkkä S. Improved Calibration of Numerical Integration Error in Sigma-Point Filters. IEEE Transactions on Automatic Control, 66(3):1286-1292.

[Journal] [arXiv]

Karvonen T, Wynne G, Tronarp F, Oates CJ, Särkkä S. Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions. SIAM Journal of Uncertainty Quantification, 8(3):926-958.

[Journal] [arXiv] [Video]

Fisher MA, Oates CJ, Powell C, Teckentrup A. A Locally Adaptive Bayesian Cubature Method. International Conference on Artificial Intelligence and Statistics (AISTATS 2020).

[Journal] [arXiv] [Video] [Video2]

South LF, Nemeth C, Oates CJ. Discussion of “Unbiased Markov Chain Monte Carlo with Couplings“. Journal of the Royal Statistical Society (Series B), 82(3):590-592.

[Journal] [arXiv]

Oates CJ, Cockayne J, Prangle D, Sullivan TJ, Girolami M. Optimality Criteria for Probabilistic Numerical Methods. In Multivariate Algorithms and Information-Based Complexity, eds, Hickernell, Kritzer, Berlin/Boston De Gruyter.

[Book] [arXiv] [Workshop]

Wang J, Cockayne J, Oates CJ. A Role for Symmetry in the Bayesian Solution of Differential Equations. Bayesian Analysis, 15(4):1057-1085.

[Journal] [arXiv]

Girolami M, Ipsen I, Oates CJ, Owen A, Sullivan T. Editorial: Special Edition on Probabilistic Numerics. Statistics and Computing, 29(6):1181-1183.

[Journal]

Hill SM, Oates CJ, Blythe D, Mukherjee S. Causal Learning via Manifold Regularization. Journal of Machine Learning Research, 20:1-32.

[Journal] [arXiv]

Chen WY, Barp A, Briol FX, Gorham J, Girolami M, Mackey L, Oates CJ. Stein Point Markov Chain Monte Carlo. International Conference on Machine Learning (ICML 2019).

[Journal] [Supplement] [arXiv] [Software] [Video]

Oates CJ, Sullivan TJ. A Modern Retrospective on Probabilistic Numerics. Statistics and Computing, 29(6):1335-1351.

[Journal] [arXiv]

Cockayne J, Oates CJ, Sullivan T, Girolami M. Bayesian Probabilistic Numerical Methods. SIAM Review, 61(4):756-789.

[Journal] [arXiv] [Video1] [Video2] [Video3] [Blog]

Best Student Paper Prize, ASA Section on Bayesian Statistical Science

Featured on the cover of the journal

Highly Cited

Karvonen T, Särkkä S, Oates CJ. Symmetry Exploits for Bayesian Cubature Methods. Statistics and Computing, 29:1231-1248.

[Journal] [arXiv] [Software]

Cockayne J, Oates CJ, Ipsen I, Girolami M. A Bayesian Conjugate Gradient Method (with discussion and rejoinder). Bayesian Analysis, 14(3):937-1012.

[Journal] [arXiv] [Software] [Webinar]

This was the first ever discussion paper webinar held by the journal.

Oates CJ, Cockayne J, Aykroyd RG, Girolami M. Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment. Journal of the American Statistical Association, 114(528):1518-1531.

[Journal] [arXiv] [Software]

Ehler M, Gräf M, Oates CJ. Optimal Monte Carlo Integration on Closed Manifolds. Statistics and Computing, 29(6):1203-1214.

[Journal] [arXiv]

Oates CJ, Cockayne J, Briol F-X, Girolami M. (2019) Convergence Rates for a Class of Estimators Based on Stein's Method. Bernoulli, 25(2):1141-1159.

[Journal] [arXiv] [Stein]

Briol F-X, Oates, CJ, Girolami, M, Osborne, MA, Sejdinovic, D. Probabilistic Integration: A Role in Statistical Computation? (with discussion and rejoinder) Statistical Science, 34(1):1-22. (Rejoinder on p38-42.)

[Journal] [Discussion1] [Discussion2] [Discussion3] [Rejoinder] [arXiv] [Video] [Poster] [Blog1] [Blog2] [Blog3] [Blog4] [Blog5] [ProbNum]

Best Student Paper Prize, ASA Section on Bayesian Statistical Science

Mukherjee S, Oates CJ. Graphical Models in Molecular Systems Biology. In Handbook of Graphical Models, eds. Maathuis M, Drton M, Lauritzen S, Wainwright M, CRC Press.

[Publisher] [Preprint]

Karvonen T, Oates CJ, Särkkä S. A Bayes-Sard Cubature Method. Advances in Neural Information Processing Systems (NeurIPS 2018).

[Journal] [arXiv]

Chen WY, Mackey L, Gorham J, Briol FX, Oates CJ. Stein Points. International Conference on Machine Learning (ICML 2018), Proceedings of Machine Learning Research, 80:844-853.

[Journal] [arXiv]

Oates CJ, Niederer S, Lee A, Briol F-X, Girolami M. Probabilistic Models for Integration Error in Assessment of Functional Cardiac Models. Advances in Neural Information Processing Systems (NIPS 2017).

[Journal] [arXiv] [Video] [Poster] [Blog]

Briol FX, Oates CJ, Cockayne J, Chen, WY, Girolami M. (2017) On the Sampling Problem for Kernel Quadrature. International Conference on Machine Learning (ICML 2017), Proceedings of Machine Learning Research, 70:586-595.

[Journal] [arXiv]

Friel N, McKeone JP, Oates CJ, Pettitt AN. (2017) Discussion of "A Bayesian information criterion for singular models". Journal of the Royal Statistical Society (Series B), 79(2):323-380.

[Journal] [arXiv]

Oates CJ, Kasza J, Simpson JA, Forbes AB. (2017) Repair of Partly Misspecified Causal Diagrams. Epidemiology, 28(4):548-552.

[Journal] [PubMed] [Software] [Video] [Erratum]

Oates CJ, Girolami M, Chopin N. (2017) Control Functionals for Monte Carlo Integration. Journal of the Royal Statistical Society, Series B, 79(3):695-718.

[Journal] [arXiv] [Blog1] [Blog2] [Supplement] [Software]

Friel N, McKeone JP, Oates CJ, Pettitt AN. (2017) Investigation of the Widely Applicable Bayesian Information Criteria. Statistics and Computing, 27(3):833-844.

[Journal] [arXiv] [Blog]

Cockayne J, Oates CJ, Sullivan T, Girolami M (2016) Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. Proceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Ed. Geert Verdoolaege, AIP Conference Proceedings, 1853:060001.

[Journal] [arXiv]

This is a short form of the full paper: Cockayne J, Oates CJ, Sullivan T, Girolami M. Probabilistic Meshless Methods for Bayesian Inverse Problems.

[arXiv] [Video] [Poster] [ProbNum] [Video]

Oates CJ, Kasza J, Mukherjee S (2016) Discussion of “Causal inference using invariant prediction: identification and confidence intervals” by Peters, Bühlmann and Meinshausen. Journal of the Royal Statistical Society (Series B), 78(5):947-1012.

[Journal] [arXiv]

Harjanto D, Papamarkou T, Oates CJ, Rayon Estrada V, Papavasiliou FN, Papavasiliou A. (2016) RNA editing generates sequence diversity within cell populations. Nature Communications, 7:12145.

[Journal]

Oates CJ, Girolami M. (2016) Control Functionals for Quasi-Monte Carlo Integration. Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research W&CP, 51:56-65.

[Journal] [arXiv] [Poster]

Selected for Oral Presentation (top 6.5% of submissions)

Oates CJ, Smith JQ, Mukherjee S. (2016) Estimation of Causal Structure Using Conditional DAG Models. Journal of Machine Learning Research, 17(54):1−23.

[Journal] [arXiv] [Supplement]

Oates CJ, Papamarkou T, Girolami M (2016) The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation. Journal of the American Statistical Association, 111(514):634-645.

[Journal] [arXiv] [Blog1] [Blog2] [Blog3]

Friel N, Mira A, Oates CJ (2015) Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods. Bayesian Analysis, 11(1):215-245.

[Journal] [arXiv] [Blog1] [Blog2]

Oates CJ, Smith JQ, Mukherjee S, Cussens J (2016) Exact Estimation of Multiple Directed Acyclic Graphs. Statistics and Computing, 26(4):797-811.

[Journal] [arXiv] [Poster] [Software]

Briol F-X, Oates CJ, Girolami M, Osborne MA. (2015) Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. Advances in Neural Information Processing Systems (NIPS 2015).

[Journal] [arXiv] [Video] [Blog1] [Blog2] [ProbNum]

Selected for Spotlight Presentation

Korkola JE, Collisson EA, Heiser L, Oates CJ, Bayani N, Itani, S, Esch, A, Thompson, W, Griffith OL,Wang NJ, Kuo W-L, Cooper B, Billig J, Ziyad S, Hung JL, Jakkula L, Lu Y, Mills G, Spellman PT, Tomlin, C., Mukherjee S, Gray JW. (2015) Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer. PLoS One, 10(7):e0133219.

[Journal]

Oates CJ. (2015) Accelerated Nonparametrics for Cascades of Poisson Processes. Stat, 4(1):183-195.

[Journal] [arXiv] [Newsletter]

Oates CJ, Simpson D, Girolami M (2015) Discussion of “Sequential Quasi-Monte Carlo” by Gerber and Chopin. Journal of the Royal Statistical Society (Series B), 77(3):555-556.

[Journal] [arXiv] [Blog]

Oates CJ, Costa L, Nichols T (2015) Towards a Multi-Subject Analysis of Neural Connectivity. Neural Computation, 27:151–170.

[Journal] [arXiv]

Oates CJ, Amos R, Spencer SEF (2014) Quantifying the Multi-Scale Performance of Network Inference Algorithms. Statistical Applications in Genetics and Molecular Biology 13(5):611-631.

[Journal] [arXiv] [Supplement]

Oates CJ, Korkola J, Gray, JW, Mukherjee S (2014) Joint Estimation of Multiple Related Biological Networks. The Annals of Applied Statistics 8(3):1892-1919.

[Journal] [arXiv]

Oates CJ, Dondelinger F, Bayani N, Korkola J, Gray JW, Mukherjee S (2014) Causal network inference using biochemical kinetics. Bioinformatics 30(17):i468-i474.

[Journal] [arXiv] [Software]

Best Paper at the European Conference on Computational Biology 2014

Oates CJ, Mukherjee S (2014) Joint Structure Learning of Multiple Non-Exchangeable Networks. Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research W&CP 33:687-695.

[Journal] [arXiv]

Casale FP, Giurato G, Nassa G, Armond J, Oates CJ, Corà D, Gamba A, Mukherjee S, Weisz A, Nicodemi M (2014) Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines. PLoS One 9(2):e88485.

[Journal]

Armond J, Saha K, Rana AA, Oates CJ, Jaenisch R, Nicodemi M, Mukherjee S (2014) A stochastic model dissects cellular states and heterogeneity in transition processes. Nature Scientific Reports 4:3692.

[Journal] [WRAP]

Oates CJ (2013) Bayesian Inference for Protein Signalling Networks. PhD Thesis.

[pdf]

Chau Y-X, Oates CJ, Rana AA, Robinson L, Nicodemi M. (2013) Self Organisation and Emergence. In: Complexity Science: The Warwick Master’s Course (London Mathematical Society Lecture Note Series). Ed. by Ball R, Kolokoltsov V, MacKay R., Cambridge University Press.

[Publisher]

Oates CJ, Mukherjee S (2012) Network Inference and Biological Dynamics. The Annals of Applied Statistics 6(3):1209-1235.

[Journal] [arXiv]

Oates CJ, Hennessy BT, Lu Y, Mills GB, Mukherjee S (2012) Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics. Bioinformatics 28(18):2342-2348.

[Journal] [arXiv]

Fisher MA, Nolan T, Graham MM, Prangle D, Oates CJ. Measure Transport with Kernel Stein Discrepancy, AISTATS 2021.

[Journal] [arXiv] [Software]

Selected or oral presentation (top 3%)

(Note that the arXiv version corrects errors in the AISTATS version.)

Cockayne J, Graham MM, Oates CJ, Sullivan TJ. (2022) Testing Whether a Learning Procedure is Calibrated. Journal of Machine Learning Research, 23(203):1-36.

[Journal] [arXiv]

Wang C, Chen WY, Kanagawa H, Oates CJ. Stein Π-Importance Sampling. Advances in Neural Information Processing Systems (NeurIPS 2023) [arXiv]

Selected for spotlight presentation.

Fisher M, Oates CJ. Gradient-Free Kernel Stein Discrepancy. Advances in Neural Information Processing Systems (NeurIPS 2023) [arXiv]

Anastasiou A, Barp A, Briol F-X, Ebner B, Gaunt RE, Ghaderinezhad F, Gorham J, Gretton A, Ley C, Liu Q, Mackey L, Oates CJ, Reinert G, Swan Y. Stein's Method Meets Statistics: A Review of Some Recent Developments. Statistical Science, 38(1): 120-139.

[Journal] [arXiv]

South LF, Oates CJ, Mira M, Drovandi C. Regularised Zero-Variance Control Variates for High-Dimensional Variance Reduction. Bayesian Analysis, 18(3): 865-888.

[Journal] [arXiv] [video]

Strocchi M, Longobardi S, Augustin CM, Gsell MAF, Vigmond EJ, Plank G, Oates CJ, Wilkinson RD, Niederer SA. Parameter Space Reduction for Four-chamber Electromechanics Simulations Using Gaussian Processes Emulators. In Proceedings of the 10th Vienna International Conference on Mathematical Modelling, 2022.

[Web]

Teymur O, Foley CN, Breen PG, Karvonen T, Oates CJ. Black Box Probabilistic Numerics. Advances in Neural Information Processing Systems (NeurIPS 2021).

[Journal] [arXiv]

Karvonen T, Oates CJ (2023) Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed. Journal of Machine Learning Research, 24(120):1−47.

[Journal] [arXiv]

Hubbert S, Porcu E, Oates CJ, Girolami M (2023) Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. Transactions on Machine Learning Research.

[OpenReview] [arXiv]

Sun Z, Oates CJ, Briol FX. Meta-learning Control Variates: Variance Reduction with Limited Data. Conference on Uncertainty in Artificial Intelligence (UAI 2023)

[arXiv]

Selected for oral presentation.

Strocchi M, Longobardi S, Augustin CM, Gsell MAF, Petras A, Rinaldi CA, Vigmond EJ, Plank G, Oates CJ, Wilkinson RD, Niederer SA. Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators. PLoS Computational Biology, 19(6): e1011257. [Journal]

Oates CJ. Minimum Kernel Discrepancy Estimators. To appear in: A. Hinrichs, P. Kritzer, F. Pillichshammer (eds.). Monte Carlo and Quasi-Monte Carlo Methods 2022. Springer Verlag. [Book] [arXiv]

Matsubara T, Knoblauch J, Briol FX, Oates CJ. (2023) Generalised Bayesian Inference for Discrete Intractable Likelihood. Journal of the American Statistical Society, 119(547), 2345–2355. [Journal][arXiv]

Reid TW, Ipsen ICF, Cockayne J, Oates CJ. Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG. Numerische Mathematik, 155, 239-288. [Journal] [arXiv]

Oates CJ. Review of "Probabilistic Numerics" by Hennig, Osborne and Kersting. SIAM Review, 65(3):905-915. [Journal]

Porcu E, Bevilacqua M, Schaback R, Oates CJ. The Matérn Model: A Journey through Statistics, Numerical Analysis and Machine Learning. Statistical Science, 39(3):469-492. [Journal] [arXiv]

Fisher MA, Teymur O, Oates CJ. GaussED: A Probabilistic Programming Language for Sequential Experimental Design. Newcastle University Technical Report. [arXiv]

Menictas M, Oates CJ, Wand MP. Online Semiparametric Regression via Sequential Monte Carlo. Australian & New Zealand Journal of Statistics, to appear. [Journal] [arXiv]

Oates CJ, Karvonen T, Teckentrup AL, Strocchi M, Niederer SA. Probabilistic Richardson Extrapolation. Journal of the Royal Statistical Society, Series B, 87(2):457-479. [Journal] [arXiv]

Bhattacharya A, Linero A, Oates CJ (2024) Grand Challenges in Bayesian Computation. ISBA Bulletin 31(3). [arXiv]

Shen Z, Knoblauch J, Power S, Oates CJ. Prediction-Centric Uncertainty Quantification via MMD. Artificial Intelligence and Statistics (AISTATS 2025) [arXiv]

Wang C, Chen W, Kanagawa H, Oates CJ. Reinforcement Learning for Adaptive MCMC. Artificial Intelligence and Statistics (AISTATS 2025) [arXiv]